"Utilização da RMN associada a métodos quimiométricos na caracterização de petróleos e derivados"

Detalhes bibliográficos
Ano de defesa: 2016
Autor(a) principal: Oliveira, Emanuele Catarina da Silva
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal do Espírito Santo
BR
Doutorado em Química
Centro de Ciências Exatas
UFES
Programa de Pós-Graduação em Química
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
54
Link de acesso: http://repositorio.ufes.br/handle/10/7359
Resumo: Petroleum products are in general highly complex andconsiderable effort is needed to characterize their chemical and physical properties. Frequently the results of several analyses are urgent and this is compromised by the way the analyses are carried out. Thus, NMR (Nuclear Magnetic Resonance) associated with chemometrics methods have been generating alternative methods to characterize and evaluate physical and chemical properties of petroleum and its derivatives with high precision, reliability and promptitude. Considering its applicability, this work proposes the use of NMR as spectroscopic method and from the integration areas of 1H and 13C NMR’s spectra associated to the chemometric calibration tools (Partial Least Square Method,PLS, and Multiple Linear Regression, MLR) it were determined the following properties in petroleum samples: API gravity, Maximum Pour Point (°C), Cinematic Viscosity (mm2/s at 50°C) and SARA-saturated, aromatic, resins and asphaltenes (%m/m),in order to evaluate the obtained models’ performance. This evaluation was obtained through determination and analyses of the coefficient of determination (R2)of several calculated errors for the calibration and prediction sets. The models were still exposed to statistical tests and their figures of merit calculated. The chemical properties with direct influence on the spectral profile, such as, API gravity, kinematic viscosity and saturated and aromatic contents in SARA models, had generated more robust models and with better predictive capacity, when compared to the models selected for the prediction of the remaining properties, maximum pour point, resins and asphaltenes. A standard recognition tool (partial least squares method with discriminant analysis, PLS-DA) was used to classify petroleum samples into light, medium or heavy oils according to API gravity. In this work, PCA (Principal Component Analysis) models were also constructed aiming the exploratory analysis of a set of spectra of oil derivative samples in order to determine acidity of the original oil considering the structural parameters of the fractions..